Quantitative Evaluation of Decision Effects in the Management of Emergency Department Problems

Abstract Due to the complexity and crucial role of an Emergency Department(ED) in the healthcare system. The ability to more accurately represent, simulate and predict performance of ED will be invaluable for decision makers to solve management problems. One way to realize this requirement is by modeling and simulating the emergency department, the objective of this research is to design a simulator, in order to better understand the bottleneck of ED performance and provide ability to predict such performance on defined condition. Agent-based modeling approach was used to model the healthcare staff, patient and physical resources in ED. This agent-based simulator provides the advantage of knowing the behavior of an ED system from the micro-level interactions among its components. The model was built in collaboration with healthcare staff in a typical ED and has been implemented and verified in a Netlogo modeling environment. Case studies are provided to present some capabilities of the simulator in quantitive analysis ED behavior and supporting decision making. Because of the complexity of the system, high performance computing technology was used to increase the number of studied scenarios and reduce execution time.

[1]  Emilio Luque,et al.  ABMS optimization for emergency departments , 2012, Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC).

[2]  W. S. Armel,et al.  The use of simulation to evaluate hospital operations between the emergency department and a medical telemetry unit , 2003, Proceedings of the 2003 Winter Simulation Conference, 2003..

[3]  Edwin D Boudreaux,et al.  Patient satisfaction in the Emergency Department: a review of the literature and implications for practice. , 2004, The Journal of emergency medicine.

[4]  Emilio Luque,et al.  A Generalized Agent-Based Model to Simulate Emergency Departments , 2014 .

[5]  Ehsan Bolandifar,et al.  An emergency department patient flow model based on queueing theory principles. , 2013, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[6]  Manel Taboada,et al.  Using an Agent-Based Simulation for Predicting the Effects of Patients Derivation Policies in Emergency Departments , 2013, ICCS.

[7]  M. Bullard,et al.  Revisions to the Canadian Emergency Department Triage and Acuity Scale implementation guidelines. , 2004, CJEM.

[8]  Mark E. Lewis,et al.  OPTIMAL CONTROL OF A TWO-STAGE TANDEM QUEUING SYSTEM WITH FLEXIBLE SERVERS , 2002 .

[9]  A. Borshchev,et al.  From System Dynamics and Discrete Event to Practical Agent Based Modeling : Reasons , Techniques , Tools , 2004 .

[10]  Averill M. Law.  A tutorial on how to select simulation input probability distributions , 2013, 2013 Winter Simulations Conference (WSC).

[11]  Fabrice Labeau,et al.  Estimating the waiting time of multi-priority emergency patients with downstream blocking , 2014, Health care management science.

[12]  Yariv N. Marmor,et al.  Emergency department operations: The basis for developing a simulation tool , 2005 .

[13]  Murray J. Côté,et al.  Patient flow and resource utilization in an outpatient clinic , 1999 .